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Challenges and Opportunities for the Design and Development of Intelligent, Sustainable and Resilient Personalized Product-Service Systems Towards Industry 5.0
Abstract
The increasing consumer demand for personalized and customized goods and services in the manufacturing sector presents significant challenges. Limited research has been conducted on integrating the mass personalization paradigm into the Industry 4.0 framework. However, there is potential to enhance collaboration between the Internet of Things (IoT), machinery, software systems, and human operators through this paradigm, which can serve as a strategy for high-tech manufacturing automation. While Industry 4.0 has primarily focused on technological advancements, the emergence of Industry 5.0 initiatives in various countries reflects a growing emphasis on human-centric aspects. Industry 5.0 represents a shift towards a people-oriented strategy aimed at creating technologies, systems, and services that promote sustainability and human well-being. This societal transformation will lead to the emergence of Society 5.0, characterized by its superintelligent nature. Within this context, the democratization of knowledge co-production is facilitated by advanced digital technologies, underpinned by three pivotal pillars: human-centricity, resilience, and sustainability. To that end, this chapter aims to explore the role of digital technologies in facilitating the design and development of Personalized Product-Service Systems (PSS) within a human-centric resilient manufacturing framework. It investigates how intelligent systems, sustainability principles, and resilient practices can be leveraged to meet the evolving needs of customers while promoting social well-being and sustainability within the Industry 5.0 paradigm. Furthermore, data-driven approaches will be explored as part of the Everything as a Service (XaaS) paradigm, enabling the analysis and utilization of large datasets to inform decision-making and enhance the customization and personalization of PSS.
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Aim: This work explores the opportunities and challenges associated with the integration of Product Service Systems (PSS) into Smart Grids (SGs) with the aim of improving manufacturing and production systems’ sustainability under the Society 5.0 framework. Methods: A bibliometric analysis was conducted to examine the existing bibliographic material and identify the primary scientific directions for this research area. The aim was to provide a thorough understanding of the research problems and perform an in-depth analysis of the distinctive aspects of development of the scientific research in this field and visualize the results with VOS viewer tool. Results: Industry, society, and academia are being reshaped and reorganized to integrate new information and communications technology (ICT) to enable complete digitization and digitalization of their infrastructures. This trend is driven by the need for more resilient, environmentally sustainable, and human-centric systems, as indicated by the terms Industry 5.0 and Society 5.0. Electrical power generation and distribution currently are critical issues for society. In this context, SGs are examined from the perspective of product-service systems (PSSs). The key enabling technologies are discussed and a technical discussion based on presentation of implemented frameworks follows. Conclusion: Intelligent societies will become the new reality. It is necessary to adapt existing infrastructures toward human centricity, resilience, and sustainability. SGs require further development, with several challenges still to be addressed. However, engineers have several technologies available, including blockchain and PSSs, to promote client activity in societal infrastructures.
Nowadays, manufacturing enterprises are struggling to satisfy personalized and dynamic user requirements. Smart Product-service System (Smart PSS), as a promising and sustainable business model, can respond to personalized and dynamic demands through effective configuration/reconfiguration processes. Mass personalization (MP), which emphasizes diversified values to improve user experience by considering both functional and affective elements, can significantly contribute to Smart PSS. However, Smart PSS field still lacks a unified knowledge representation framework and an effective configuration method in the MP context. Aiming to fulfill the gap, this research proposes a novel Smart PSS configuration method oriented to MP to meet personalized and dynamic requirements of users, especially satisfy affective and functional requirements simultaneously throughout the reconfiguration life-cycle. Due to the extreme complexity of heterogeneous data from Smart PSS and MP, Knowledge Graph (KG) is introduced as a powerful tool for managing intricate system knowledge. The common schemas of three KGs are constructed as the basis of configuration, with design knowledge, related field knowledge, and user information integrated comprehensively. KG-based question answering is combined with similarity calculation as a hybrid method in the configuration process to provide satisfactory and complete solutions. Moreover, the configuration process is placed in the reconfiguration life-cycle of Smart PSS to deal with dynamic changes through the latest knowledge in updating KGs. Furthermore, an illustrative case study of the intelligent management system for family medication is demonstrated. Through the comparison with other approaches, the proposed method is proved to be valuable for the implementation of Smart PSS configuration for MP.
Industry 5.0 is a combination of organisational principles and technologies to design and manage operations and supply chains as resilient, sustainable, and human-centric systems. While the general notion of Industry 5.0 has been elaborated, its implications for future operations and supply chains remain underexplored. This paper contributes to the conceptualisation of Industry 5.0 from the perspective of viability. We contextualise a framework of Industry 5.0 through the lens of the viable supply chain model, the reconfigurable supply chain, and human-centric ecosystems. Our study uncovers the major dimensions that characterise Industry 5.0 as a technological-organisational framework. First, the major technological principles of Industry 5.0 are collaboration, coordination, communication, automation, data analytics processing, and identification. Second, Industry 5.0 covers four areas: organisation, management, technology, and performance assessment. Third, Industry 5.0 spans three levels: society level, network level, and plant level. Last but not least, Industry 5.0 frames a new triple bottom line: resilient value creation, human well-being, and sustainable society. We provide a definition of Industry 5.0 and discuss its implications by elaborating on the understanding of value in Industry 5.0, which spans the dimensions of profit, people, and society. We also discuss open research areas.
Industry 4.0 has been provided for the last 10 years to benefit the industry and the shortcomings; finally, the time for industry 5.0 has arrived. Smart factories are increasing the business productivity; therefore, industry 4.0 has limitations. In this paper, there is a discussion of the industry 5.0 opportunities as well as limitations and the future research prospects. Industry 5.0 is changing paradigm and brings the resolution since it will decrease emphasis on the technology and assume that the potential for progress is based on collaboration among the humans and machines. The industrial revolution is improving customer satisfaction by utilizing personalized products. In modern business with the paid technological developments, industry 5.0 is required for gaining competitive advantages as well as economic growth for the factory. The paper is aimed to analyze the potential applications of industry 5.0. At first, there is a discussion of the definitions of industry 5.0 and advanced technologies required in this industry revolution. There is also discussion of the applications enabled in industry 5.0 like healthcare, supply chain, production in manufacturing, cloud manufacturing, etc. The technologies discussed in this paper are big data analytics, Internet of Things, collaborative robots, Blockchain, digital twins and future 6G systems. The study also included difficulties and issues examined in this paper head to comprehend the issues caused by organizations among the robots and people in the assembly line.
In the era of Industry 4.0, manufacturing and production systems were revolutionized by increasing operational efficiency and developing and implementing new business models, services, and products. Concretely, the milestone set for Industry 4.0 was to improve the sustainability and efficiency of production systems. By extension, the emphasis was focused on both the digitization and the digitalization of systems, providing room for further improvement. However, the current technological evolution is more system/machine-oriented, rather than human-oriented. Thus, several countries have begun orchestrating initiatives towards the design and development of the human -centric aspect of technologies, systems, and services, which has been coined as Industry 5.0. The impact of Industry 5.0 will extend to societal transformation, which eventually leads to the generation of a new society, the Society 5.0. The developments will be focused on the social and human-centric aspect of the tools and technologies introduced under the framework of Industry 4.0. Therefore , sustainability and human well-being will be at the heart of what comes next, the Industry 5.0, as a subset of Society 5.0. Industry 5.0 will build on the foundations laid during Industry 4.0 by emphasizing human-centered, resilient, and sustainable design. Consequently, the authors in this research work, through a critical literature review, aim to provide adequate reasoning for considering Industry 5.0 as a framework for enabling the coexistence of industry and emerging societal trends and needs. The contribution of this research work extends to the provision of a framework to facilitate the transition from Industry 4.0 to Society 5.0.
Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the f−test, therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities.
Modern manufacturing and production companies have transformed their business models in order to integrate Product-Service Systems (PSS). These changes in the modus operandi of companies facilitates the close collaboration of manufacturers and customer in the development of new product. Furthermore, the recent research works in the field of simulation indicate that the integration of Digital Twin, in various phases of the product lifecycle can be very beneficial, saving valuable time. More specifically, the simulation space provided by the Digital Twin can facilitate engineers in the process of information fusion derived from the virtual model of the product (i.e. conceptual model), knowledge gathered from the usage of the physical products as well as from information provided by the customers during the design of a new product. On the other hand, Extended Reality (XR) applications, including Virtual Reality (VR) can facilitate engineers in developing intuitive applications for capturing end-users’ knowledge through the interaction with the holograms displayed on their field of view, i.e. the physical space. Moreover, with the utilization of VR, engineers are capable of visualizing the simulation results from the Digital Twin and collaborate with their colleagues for improving new product designs. Towards this direction, the contribution of this research work is focused on the design and development of a framework for including customers in the design phase of new products under the mass personalization paradigm. The Digital Twin of the new product will accept as input information regarding the user-defined requirements, as well as data from previous similar design cases. Through the development of a VR application, engineers can visualize the simulation results and remotely collaborate during the design of new products. Finally, the development of an Augmented Reality (AR) application for the requirement elicitation from customers and the visualization of simulation results is provisioned.
Under the framework of Industry 4.0, machines and machine tools have evolved to smart and connected things, comprising the Internet of Things (IoT) and leading to the Mass Per-sonalization (MP) paradigm, which enables the production of uniquely made products at scale. MP, fueled by individualization trends and enabled by increasing digitalization, has the potential to go beyond current mass customization. Although this evolution has enabled engineers to gain useful insight for the production, the machine status, the quality of products, etc., machines have become more complex. Thus, Maintenance Repair and Overhaul (MRO) operations should be un-dertaken by specialized personnel. Additionally, Augmented Reality (AR) can support remote maintenance assistance to handle unexpected malfunctions. Moreover, due to advances regarding Product Service Systems (PSS), manufacturing companies are offering many services to improve user experience. Consequently, in this manuscript the design and development of a method based on the principles of servitization for the provision of an intelligent and adaptable maintenance service assisted by AR are presented. The contribution of the manuscript extends to the provision of an optimization algorithm for adapting the schedules of the stakeholders based on the energy supplier predictions. The developed method was tested and validated on an industrial case study of injection mold maintenance, achieving 11% energy reduction, 50% less time for mold inspection , and a 20% rise in on-time mold deliveries.
The study presents the XaaS architecture and explores how this business model has strategically developed and how it has revolutionized the whole business industry. The study indicates how this particular business model allows companies to enhance the range of advantages of Internet Cloud model applications, particularly when it deals with process of media processing and customization of users. Its huge benefits with negligible disadvantages are mentioned as part of the presented research. This study will help to understand the services of XaaS model, challenges experiencing by it and its future opportunities. This will also assists new scholars of field to build a better understanding and better prospects regarding XaaS in future.
The energy market is extended to an open and cross-border level and has increased the competition among the energy supply companies. The energy suppliers focus on the provision of smarter solutions by using the Industry 4.0 digital technologies to industrial customers. These services are linked to energy consumption and could affect the operational costs of the SMEs and suppliers. Therefore, in this paper an energy Industrial Product Service System (IPSS) framework is proposed, towards the creation of a Smart service-based energy ecosystem. The proposed framework has been validated in a real-life case study with a European electricity distributor company.
Industrial smart product-service system (ISPSS), as an emerging industrial digital servitization paradigm, has attracted ever-increasing attentions from both industries and academics recently. The prevailing adoption of cutting-edge information and communication technologies, digital technologies and artificial intelligence has enabled the engineering lifecycle management in an ever-smarter manner with context-awareness. Nevertheless, little study has provided any systematic process to prescribe its sustainable development throughout the engineering lifecycle, let alone any to consider sustainability in the cyber space other than the physical components. Aiming to fill the gap, this work outlines the definition, key features and social, economic and environmental concerns of the proposed sustainable ISPSS, and further proposes a systematic 4-phase cyber-physical development framework by referring to the ISO14001:2015 standards. As a short communication, it is hoped this research can attract more open discussion and in-depth investigation towards sustainable ISPSS development in the near future.
Ubiquitous and pervasive computing holds great potential in the domain of Product-Service Systems to introduce a model-driven paradigm for decision support. Data-driven design is often discussed as a critical enabler for developing simulation models that comprehensively explore the PSS design space for complex systems, linking of performances to customer and provider value. Emerging from the findings of two empirical studies conducted in collaboration with multinational manufacturing companies in the business-to-business market, this paper defines a data-driven framework to support engineering teams in exploring, early in the design process, the available design space for Product-Service Systems from a value perspective. Verification activities show that the framework and modeling approach is considered to fill a gap when it comes to stimulating value discussions across functions and organizational roles, as well as to grow a clearer picture of how different disciplines contribute to the creation of value for new solutions.
By adopting PSS paradigm (Product–Service System), companies are growing to new business fields that require hard changes in the industrial practices. PSS realizes smart integration of physical, cyber and organizational elements to deliver added value to the customer, through a long-term relationship and solution-centered offers. This complexity makes the management of whole PSS lifecycle challenging. The paper presents a new development aided tool, part of the ICP4Life European platform, as an innovative answer to this challenge. Designed as a collaborative knowledge-based solution, the platform originality is to support all PSS design, production and usage planning of industrial PSS, at both methodological and technical aspects. The main idea is to encapsulate a collection of potential verified solutions as knowledge fragment, able to achieve a product–service with certain performance value with regard to a set of requirements. This knowledge asset is then used to support collaboratively the stakeholders by several innovative functionalities along the PSS development project.
During the last few decades, the PSS literature has documented industrial firms’ transformation from the product dominant logic of business to product-service bundles constituted by machines and related services. This transformation has had several dramatic implications on firms’ profitability, strategy, operations, organizational setting, sales and marketing approaches, and R&D practices. However, more recently, industrial firms have started to adopt various smart technologies that are embedded within the PSS. The use of smart technologies in PSS gives rise to the new types of PSS that are referred to in this paper as Smart PSS. Based on a literature review of 43 papers from relevant academic fields, this paper seeks answer to the following research question: what are the value creating features of smart product service systems (Smart PSS) in industrial firms? We synthesize the knowledge on Smart PSS to provide a definition and show the distinctive features of Smart PSS and propose an agenda for future research.
Humans are a vital element to automotive manufacturing; however, skilled production personnel have largely been designated as data receivers in Cyber-Physical Systems (CPS) of Industry 4.0. A renewed focus on the human worker who completes significant portions of manual value-added content in automotive assembly through Cyber-Human Systems (CHS) is allowing humans to perform their jobs more safely, efficiently, and supporting enhanced control and quality monitoring of manual manufacturing tasks. There is a need for a unified complementary framework of CHS and CPS to guide the implementation of future smart manufacturing systems.
A Product–Service System (PSS) is a dynamic and multidimensional system that is comprised of products, services, a large network of stakeholders and all necessary supporting infrastructures, aiming towards becoming continuously competitive, satisfying customer needs and improving environmental aspects. The design of such a system and its evaluation throughout the lifecycle are challenging tasks owing to their inherent complexity, which becomes even greater when the aspect of customisation is considered. Nevertheless, the literature work regarding the customisation of PSS is scarce. Towards bridging this gap, the present work proposes a framework for PSS customisation, including the main steps that are required for a successful PSS customisation. For each step in the framework, which has been collected from the literature, PSS approaches have been provided, and could be used in the execution of each step. Next, the present study introduces a method for: (i) the modular representation of PSS based on vector analysis, and then (ii) the quantification of the PSS customisation degree.
Over the last decade, Product-Service System (PSS) has been established as a prominent business model which promises sustainability. A great amount of literature work has been devoted to PSS issues, but there is fairly limited published work on integrated and easily applicable evaluation methodologies for PSS design, as well as a lack of Lean PSS approaches. Contributing to these directions, the present work introduces a framework for the evaluation and improvement of the Lean PSS design using key performance indicators (KPIs), Lean rules, and sentiment analysis, aiming to feed all the stages of PSS design lifecycle. According to the evaluation phase, a certain appropriate set of KPIs is selected and suggested to the PSS designer via a context-sensitivity analysis (CSA) tool through a pool, which have been identified after intensive literature survey, and systematically classified into five main categories: design, manufacturing, customer, environment, and sustainability. According to the same phase, sentiment analysis has been used to identify the polarity of the customer opinions regarding the PSS offerings. During the phase of Lean design assistance, Lean rules are selected using CSA and are suggested to the designer to ensure the minimization of wasteful activities. Enabler for the context awareness is the availability of feedback gathered from the manufacturing, shop-floor experts, and the different types of customers (business or final-product consumers), as well as the PSS lifecycle which the designer treats. The proposed framework is implemented in a software prototype and is applied in a mold-making industrial case study.
Lean PSS (Product-Service System) design comprises a promising strategy for delivering sustainable PSS offerings, considering several well-established lean practices. However, automated ways to apply lean practices and more specifically lean rules in industry are limited. This work proposes a methodology for improving the leanness of PSS design, by combining real-time KPI monitoring with lean principles and practices. Through a correlation of typical wastes with the metrics used in the calculation of KPIs, the Total Leanness Index (TLI) of the procedures is defined. Based on automatically identified trade-off values for TLI, lean rules are extracted to improve the performance of PSS lifecycle phases. The proposed lean rules extraction methodology (LeanREM) is validated through a case study of power waste reduction and the concurrently maintenance time decrease in a mould-making company.
Over the last decade, Product-Service System (PSS) has been established as a prominent business model which promises sustainability for both customers and organizations. A great amount of literature work has been devoted to PSS issues, but there is fairly limited published work on integrated and easily applicable evaluation methodologies for PSS design, as well as lack of Lean PSS approaches. Contributing to these directions, the present work introduces a conceptual methodology for the evaluation and improvement of the PSS design procedure using Key Performance Indicators (KPIs) and Lean rules respectively, applied throughout all stages of PSS design lifecycle. The methodology contains two phases: i) PSS design evaluation, and ii) Lean PSS design assistance. According to the evaluation phase, a certain appropriate set of KPIs is selected and suggested to the PSS designer via a context sensitivity analysis (CSA) tool through a pool of 170 KPIs, which have been identified after intensive literature survey, and systematically classified into four main categories: Design, Manufacturing, Customer, and Environmental. From the collected and classified KPIs, a successive grouping is carried out of those KPIs that contribute to the Sustainability of the designed PSS offering. During the phase of Lean design assistance, Lean Rules are selected using CSA, and suggested to the designer at all design steps to ensure the minimization of wasteful activities. Enabler for the context awareness is the availability of feedback gathered from the manufacturing, shop-floor experts and the different types of Customers (Business or final-product Customers), as well as the PSS lifecycle stages which the designer treats. An ontology knowledge model for KPIs and Lean rules is proposed to support the CSA. The methodology is discussed in a case study of the PSS design from a mould-making industry.
The end of the course evaluation has become an integral part of education management in almost every academic institution. The existing automated evaluation method primarily employs the Likert scale based quantitative scores provided by students about the delivery of the course and the knowledge of the instructor. The feedback is subsequently used to improve the quality of the teaching and often for the annual appraisal process. In addition to the Likert scale questions, the evaluation form typically contains open-ended questions where students can write general comments/feedback that might not be covered by the fixed questions. The textual feedback, however, is usually provided to teachers and administration and due to its nonquantitative nature is frequently not processed to gain more insight. This paper aims to address this aspect by applying several text analytics methods on students’ feedback. The paper not only presents a sentiment analysis based metric, which is shown to be highly correlated with the aggregated Likert scale scores, but also provides new insight into a teacher’s performance with the help of tag clouds, sentiment score, and other frequency-based filters.
The evaluation of a Product-Service System (PSS), particularly during its design phase but also throughout its lifecycle, is a challenging problem. It exhibits dynamic nature and is multidimensional, since a large network of stakeholders are usually involved in the design and development of PSS eco-systems and their influence must be considered during the evaluation of the PSS offering. Nevertheless, an effective PSS evaluation can determine the market success of the PSS offering, leading to fewer design modifications and shortened time-to-market. Still, existing literature lacks an exhaustive review and classification of PSS evaluation approaches. Moreover, a lack of critical in-depth validation of performance approaches in real industrial environments is observed. Towards bridging this gap, the present work identifies the most important perspectives in the PSS ecosystem, such as supplier selection and service level, and discusses the utilised evaluation approaches used for each of them. Based on this review, a classification of existing PSS evaluation approaches is provided, by mapping these PSS eco-system perspectives to the methods most frequently used for their evaluation. The results can be used as guidelines for PSS vendors to select the most suitable method for the evaluation of their PSS offerings.
Companies are increasingly selling functionalities of integrated solutions of products and services instead of selling mere products. This integration of products and service solutions is known as Product Service Systems (PSS), which allows to maximize customer value and overcome increasing competition in the manufacturing sector. In this sense, PSS designers require adapted tools to support the design of sustainable solutions. This article aims to address this question by proposing a methodological framework for integrating sustainability considerations into the PSS design process. To this end a review of the peculiarities of the PSS design process was performed and which aims to explore potential ways of integrating sustainability considerations within PSS design. Several requirements have been drawn from PSS design and sustainability literature which contributed towards the formalization of a methodological framework supporting the design of sustainable PSS.
In the globally competitive market landscape, companies, in order to remain sustainable, must shift their focus towards constantly improving their PSS offerings by incorporating lean thinking approaches. PSS are complex, dynamic, and multi-dimensional systems, which are supported by appropriate infrastructure and networks. At the same time, PSS require robust engineering methods and tools for their design, which allow multi-dimensional exchange of knowledge between the designers and the relevant stakeholders across the value chain. Currently, enabling technologies such as Cloud technology, social media and networking, knowledge management, and context sensitivity present new possibilities to enhance the knowledge exchange and the collaborative process of PSS design. These technologies allow the gathering and analysis of big volumes of data from scattered heterogeneous IT systems and, at the same time, allow PSS design improvements by processing the experiences of the business customers, consumers, designers, shop-floor, and provide feedback to the design phase. However, a number of challenges are encountered during the gathering, visualization, monitoring, and assessment of related data, such as the lack of context sensitivity. Simultaneously, PSS evaluation approaches are still immature and there is limited work that correlates the impact of the entire PSS lifecycle with the performance assessment of the PSS design. The present work includes a review of the aforementioned technologies from academic, market, and industrial perspectives. Based on an extensive literature review and on constructive discussions with three European SMEs, several barriers and limitations of adopting the aforementioned technologies in practice are identified. Finally, a conceptual eco-innovative framework for lean PSS design is presented, explaining how these technologies could be combined for the improvements of PSS design and evaluation procedures.
A fundamental change in how businesses are conducted and operated is an essential factor to reach more sustainable solutions not only on the industry level but also for the entire society. In this sense, the importance of developing and implementing sustainable product service system (S-PSS) business models is increasing as the number of studies are presenting it. A S-PSS business model is an approach to achieve benefits in the three dimensions of sustainability. Through efficient resource utilization and dematerialization, this type of sustainable business model helps to embed environmental and social aspects into strategic business goals and processes while increases competitive advantage. However, most of the studies that treats PSS business models do not mention their relation to sustainability. In addition, different studies take for granted that implementing PSS leads to benefits in the three sustainability dimensions. Nonetheless, PSS business models can even have a negative effect on the environment while maintaining only economic benefits. Such contradictions and incoherencies hinder the potential of PSS business models to provide sustainability benefits and might be a barrier for its adoption. Therefore, the question this study pursues is: What are the factors that make a PSS a sustainable business model? Through literature analysis, this paper proposes the identification of a set of factors that contributes to the sustainability of PSS business models. The factors identified are investigated in an exemplary case of a bike sharing system.
The product-service system (PSS) is a business system in which its integrated products and services jointly fulfill customer needs. This research proposes an evaluation scheme for PSS models. The PSS model evaluation scheme consists of evaluation criteria and methods. The current paper mainly focuses on the introduction of the evaluation criteria and their application. The set of evaluation criteria has a four-layered hierarchical structure which has 2 perspectives, 5 dimensions, 21 categories, and 94 items in total. They are designed to consider the provider and customer perspectives, and all 3P (profitability, planet, and people) dimensions. They cover various stages of a PSS lifecycle, namely, design, production, sales (or purchase), usage as well as disposal. To illustrate the usefulness of the proposed evaluation scheme, a few PSS cases are first modeled using an existing PSS visualization tool, and then evaluated using the scheme. Case studies show the proposed evaluation scheme is workable to assess the potential value of the PSS models in question; it provides an extensive knowledge base for PSS evaluation, thereby serves as an efficient and effective aid to practitioners for successful PSS development.
Recently, industries have shifted their focus on the combined ecosystem of products-services. The innovative business strategy of PSS provides an integrated solution that gives the potential for sustainability gains for both company and customer. Similarly to other business aspects, the PSS efficiency design is evaluated through performance indicators, during the phase of concept evaluation, which is the last phase before launching a product-service. This phase plays a critical role for the success of a PSS. A successful evaluation prevents design modifications or redesign, significantly reduces the cost and lead time of PSS development. There is however, limited work on integrated evaluation approaches for PSS design models, and also a lack of a collective accounting of the most important key performance indicators (KPIs) devoted on PSS. The present work attempts to contribute in these directions, and proposes a conceptual framework for the effective evaluation of PSS design using important KPIs.
As for conventional products, the profit generation and the market success of Product Service Systems (PSS) critically depend on the decisions taken during the initial lifecycle stages, when PSSs are conceptualized, designed, developed and engineered. Successful cases show the adoption of lean techniques in the early stages of products development, impelling the authors to assess the application of the same approaches also to PSS development. For this reasons the paper aims to report the state of the art of PSS Design research, relating this strategic process to the Lean Thinking approaches typically applied in traditional Product Development and Manufacturing. The literature about PSS is classified and Lean Thinking evolvement from product manufacturing to design phases is described. On this basis, the paper defines which are the aspects of Lean Thinking already applied in PSS Development also uncovering gaps and lacks of the methods proposed by the scientific literature so far. This opens the way to new opportunities and challenges through many further research and industrial projects.
Sustainable Product-Service Systems (S.PSS) carry great potential to deliver social well-being and economic prosperity while operating within the limits of our planet. They can however be complex to design, test, implement and bring to the mainstream. To increase our understanding of the potential benefits, drivers and barriers in S.PSS design, the research community has been inspired to collect and analyse an extensive number of cases in diverse sectors and to develop and test several design methods and tools. This Special Volume on “New Design Challenges to Widely Implement ‘Sustainable Product-Service Systems’” presents results of key studies in the following areas: user satisfaction and acceptance of S.PSS solutions, how industrial partnerships and stakeholder interactions can be designed for environmental and socio-ethical benefits, how knowledge of socio-technical change and transition management feeds S.PSS design processes, and the role of policy instruments to foster their implementation and scale-up. This Introduction reviews the current state of research and summarises the articles presented. The articles demonstrate increasing confidence in integrating approaches and theoretical frameworks from other arenas. These approaches include sociological practice theory, to shed new light on consumer practices in S.PSS configurations, and strategic niche management, to foster a suitable design and experimentation milieu. Experimentation, iteration and cyclical design processes were also seen by many authors as crucial to implementing and stabilising S.PSS solutions, but also their continuous sustainability evaluation. Several articles highlight the importance of local authorities, in developing S.PSS-enabling policies as well as supporting novel networks of stakeholders in the co-production of value. Finally this Introduction highlights key issues for a design research agenda, including but not limited to the development of new knowledge to support S.PSS designers (such as S.PSS design in low and middle-income contexts) and the role of Higher Educational Institutions (HEIs) in the diffusion of knowledge and know-how to companies. Together, the papers in this special volume provide insight into the promise of the S.PSS concept for understanding, advancing and accelerating sustainability.
Purpose
– The advent of recession at the beginning of twenty-first century forced many organizations worldwide to reduce cost and to be more responsive to customer demands. Lean Manufacturing (LM) has been widely perceived by industry as an answer to these requirements because LM reduces waste without additional requirements of resources. This led to a spurt in LM research across the globe mostly through empirical and exploratory studies which resulted in a plethora of LM definitions with divergent scopes, objectives, performance indicators, tools/techniques/methodologies, and concepts/elements. The purpose of this paper is to review LM literature and report these divergent definitions, scopes, objectives, and tools/techniques/methodologies.
Design/methodology/approach
– This paper highlights various definitions by various researchers and practitioners. A total of 209 research papers have been reviewed for the research contribution, research methodology adopted, tools/techniques/methodologies used, type of industry, author profile, country of research, and year of publication.
Findings
– There are plethora of LM definitions with divergent objectives and scope. Theory verification through empirical and exploratory studies has been the focus of research in LM. Automotive industry has been the focus of LM research but LM has also been adopted by other types of industries also. One of the critical implementation factors of LM is simultaneous adoption of leanness in supply chain. LM has become an integrated system composed of highly integrated elements and a wide variety of management practices. There is lack of standard LM implementation process/framework.
Originality/value
– The paper reviews 209 research papers for their research contribution, research methodology, author profile, type of industry, and tools/techniques/methodology used. Various characteristics of LM definitions are also reviewed.
Business-to-business Product Service System (PSS) solutions are characterized by intensive interaction among PSS providers, suppliers, and customers, as well as an integration of physical and immaterial solution modules. These factors increase the complexity and interdisciplinary of such offerings. Furthermore, the characteristics complicate sustainability assessment and monitoring of PSS, as environmental, social, and economic concerns have to be integrated into a PSS by minimizing negative impacts of the PSS on these three axes throughout the PSS lifecycle. The indicator-based framework “PSS Sustainability Assessment and Monitoring (PSS-SAM)” has been specifically developed to support managers and decision makers in dealing with the complex situation described above. This framework considers the most important sustainability aspects throughout the entire PSS lifecycle and adheres to European standard requirements. The results of a previous work have already confirmed the usability of the framework to assess the sustainability of one PSS module (availability of spindle) within a single PSS lifecycle phase (operating phase). The aim of this paper is to present the improvements of the PSS-SAM framework that facilitate sustainability assessment of PSS solutions with multiple modules throughout their entire lifecycle. The improved framework has been validated by a case study of an industrial micro-production PSS solution including multiple modules (spindle, maintenance, training, etc.). The long-term goal is to develop IT solutions that support the sustainability assessment of PSS.
Product-service system (PSS) design focuses on customer value and satisfaction more than traditional product or service design, and pays much attention to making improvement strategies due to the immaturity of engineering design methodology. Customer satisfaction evaluation attracts PSS providers' attentions in supporting PSS design. Importance-performance analysis (IPA) as an effective customer satisfaction evaluation tool is revised and used to identify PSS improvement strategies in this paper. The new IPA is proposed for three reasons. First, considering the fact that the attribute performance and importance are not independent variables and attribute performance has a nonlinear relationship with the overall satisfaction, Kano's model is integrated into IPA. Second, to overcome the drawbacks of statistic method and artificial neural network (ANN) in obtaining attribute importance implicitly, e.g. requiring sufficient and confident data, and overlooking the attribute original importance about attribute's contributing level to customer value realization, a set of adjustment models are proposed to revise the attributes original importance according to the Kano quality categories of attributes and the levels of attributes performance. Third, considering the mutual influence relationships among attributes, the proposed IPA takes these relationships into account by decision making trial and evaluation laboratory (DEMATEL). In addition, to deal with the uncertainty and vagueness in evaluation process, vague sets are employed in the revised IPA. A case study is carried out to demonstrate the effectiveness of the developed customer satisfaction evaluation approach.
Recently, the futuristic industry and society have caught increasing attention, that is, on Industry 5.0 and Society 5.0. Industry 5.0 is announced by European Commission toward a sustainable, human-centric, and resilient European industry. Society 5.0 is proposed by Japan Cabinet to balance economic advancement with the reso�lution of social problems in Japanese society. Generally, the revolutions of industry and society have profoundly
interacted with each other since the first industrial revolution. The coexistence of Industry 5.0 and Society 5.0 could raise varying confusions to be clarified and a series of questions to be answered. Therefore, we attempt to present the comparison, complementation, and co-evolution between Industry 5.0 and Society 5.0 to address the
corresponding foundational arguments about Industry 5.0 and Society 5.0, which could be the basic inspiration for future investigation and discussion and accelerate the development of Industry 5.0 and Society 5.0.
Construction schedules are written instructions of construction execution shared between stakeholders for essential project information exchange. However, construction schedules are semi-structured data that lack semantic details and coherence within and across projects. This study proposes an ontology-based Recurrent Neural Network approach to bi-directionally translate between human written language and machinery ontological language. The proposed approach is assessed in three areas: text generation accuracy, machine readability, and human understandability. This study collected 30 project schedules with 19,589 activities (sample size = 19,589) from a Tier-1 contractor in the UK. The experimental results indicate that: (1) precision and recall of text generation LSTM-RNN model is 0.991 and 0.874, respectively; (2) schedule readability improved by increasing the semantic distinctiveness, measured using the cosine similarity which was reduced from 0.995 to 0.990 (p < 0.01); (3) schedule understandability improved from 75.90% to 85.55%. The proposed approach formalises text descriptions in construction schedules and other construction documents with less labour investment. It supports contractors to establish knowledge management systems to learn from historic data and make more informed decisions in future similar scenarios.
Smart manufacturing refers to an advanced mode of manufacturing, which incorporates computer-integrated manufacturing (CIM) and artificial intelligence (AI) for data-enabled adaptability throughout the production cycle, from product design over process scheduling, control, and optimization to product quality assurance. Enabling this mode of manufacturing are two essential techniques: smart scheduling and predictive maintenance. For Industry 4.0 (I4.0)-based manufacturing systems, all resources (e.g., machines, robots, vehicles, materials, etc.) in a smart factory are represented as cyber-physical systems (CPS), i.e., physical entities equipped with digital identification such as RFIDs, sensors, edge computing electronics, etc. Supported by AI, this new manufacturing paradigm provides new opportunities for scheduling production resources and predictive maintenance. This chapter focuses on describing cutting-edge tools, technologies, and infrastructure that enable the transition from traditional production scheduling and time-based maintenance to smart scheduling and predictive maintenance, which ultimately enable smart manufacturing.
In the age of the industrial internet, manufacturers of industrial equipment compete through the offering of digital product-service solutions. These are built integrating smart connected products (SCP) and data-driven services, such as remote control, data analytics, diagnostic and predictive maintenance services. These services strongly influence the value created in the customer context and, consequently, the attractiveness of the integrated offering. A great issue remains about how these services should be designed by manufacturers of industrial equipment. Through integrative literature review over multiple domains, this paper provides a conceptual model that shows: i) which benefits can be created from smart product-service systems offerings at both business and individual level, in the customer organization; ii) how these benefits are linked to the choices about design and delivery of data-driven services. In respect to the second point, this study merges service science theory and data management to show how the data autonomously collected by SCP can be transformed into insights that deliver value, as far as the customer and the supplier interact, share resources and apply their specialized competences. Applying these concepts, we elaborate four exemplary archetypes of data-driven services that differ in respect to the kind of customer-supplier interactions along this data life cycle, bringing these options to different kind of customer value co-creation. Linking the mechanisms of value co-creation to design options of data-driven services, this paper has therefore notably implications for the research on servitization and smart product-service systems.
The paradigm shift from mass production to on-demand, personalized,
customer-driven, and knowledge-based production reshapes manufacturing.
Smart manufacturing leads to an automated world that relies
more on information and communication technologies (ICTs) and sophisticated
information-technology-intensive processes, enhancing flexibility.
Furthermore, as automation and digital supply chain management
become the norm across enterprise systems, advanced manufacturing
becomes increasingly linked at a global level. Manufacturing companies
are under pressure to achieve the goals of high competitiveness and
profitability in a globalized and volatile environment. To address these
challenges, engineers have to develop and implement new design and operation
methodologies for production networks taking also into account
mass personalization and market uncertainty. In the era of digitalization,
the integration of cloud-based approaches can elevate enterprise performance.
Therefore, to meet these challenges, new technologies such as
cyber-
physical systems (CPS), artificial intelligence (AI), augmented reality
(AR), big data analytics, the Internet of things (IoT), and the industrial
Internet of things (IIoT) must be integrated.
The book consists of 12 chapters, written by leading researchers in the
field of manufacturing. Chapter 1 presents the peculiarities of the integration
of key Industry 4.0 technologies toward the design, planning, and operation
of global production networks and the integration of the customer
to the design phase of the products, services, and systems. In Chapter 2
recent and future trends of how emerging technologies support the transformations
in reconfigurable supply chains and production systems are
presented. Chapter 3 is devoted to present the implications for the design
and management of global production networks (GPNs) induced by the
mass production paradigm (MPP). Chapter 4 aims to identify and highlight
the implications in the design and planning of manufacturing networks
in the mass personalization environment and Chapter 5 presents
the state of the art on adaptive scheduling and developments in smart
scheduling within Industry 4.0 paradigm. Chapter 6 presents how modern
digital manufacturing technologies may be utilized for reducing and
eventually taming the complexity in production systems and networks.
Chapter 7 provides an overview of innovative smart scheduling and predictive
maintenance (PdM) techniques under smart manufacturing production
environments. Chapter 8 reviews the landscape of the industrial Internet of things (IIoT). Chapter 9 presents a generic framework for
industrial big data utilization in industrial environments and big data
application areas and Chapter 10 aims to map major architectures and applications
of digital twins for Industry 4.0. Chapter 11 reviews and presents
machine learning (ML) technologies and artificial intelligence (AI) in
manufacturing systems. Finally, Chapter 12 demonstrates the real-world
applicability of blockchain potential using industrial case studies.
Contemporary Industries are generating a huge amount of data coming from a high variety of sources, such as machine controllers, corporate systems, sensors, operators etc. Therefore, the need for the utilization of the information generated, also known as Big Data, has emerged. Although data importance is well known, very often in manufacturing industries data are abundant and often underexploited leading to poor performance and efficiency. Big data are part of Industry 4.0 concept and also serve as key element for the Industry 4.0 technologies. But still there is a lot of ground to be covered in the exploitation and industrialization of those technologies. Modelling, Design and Operation of modern Manufacturing Systems, where Industry 4.0 technologies are applied, are three fundamental processes in the contemporary manufacturing domain that require constant attention and continuous optimization in order for the manufacturer to stay competitive and improve the efficiency and reduce the environmental impact.
The digitalization of modern manufacturing systems has resulted to increasing data generation, also known as Big Data. Although there are several technologies and techniques under the term Data Analytics for gathering such data, their interpretation to information, and ultimately to knowledge remains in its infancy. Consequently, albeit engineers, currently can monitor the factory level, optimization is cut off of the data acquisition, and is based on data related methodologies. The focus should be pivoted on designing and developing suitable frameworks for integrating Big Data to process optimization based on the context of information gathered from the shopfloor. This paper aims is to investigate the opportunities and the gaps as well as the challenges arising in the current industrial landscape, towards the efficient utilization of Big Data, for process optimization based on the integration of semantics. To that end, a literature review is performed, and a data-based framework is presented.
The demand response monitoring is one key energy-efficient production technique and can reduce costs contributing simultaneously to the reduction of environmental emissions and maintaining the stability of electrical grids following the policies examined from European and International organizations. Although, energy trade market effect has been investigated in the literature, the novelty of this research work lies in the design and development of an Industrial Product Service System (IPSS) business model, aiming to the collaboration between Energy Sales Companies (ESC) and manufacturing companies. ESC through an energy-demand management tool, supported by data acquisition devices connected to the Cloud, calculates and signals energy price forecasts to the manufacturers. Moreover, an adaptive production scheduling approach considering the power usage of manufacturers in response to time-varying energy prices is presented. Particularly, an intelligent algorithm is applied for the generation and examination of multi-criteria alternative assignment scenarios and an evaluation of each planning scenario against a set of user-defined criteria is made for the identification of the most efficient ones. The proposed approach is validated in an industrial case study using production and energy-consumption data from a mold making industry. The results indicate potential savings for the industrial consumer finding both actors to mutually benefit.
Sustainable development requires the creation and development of new business models. Sharing economy will maximize the use of a product by several people, while it enables the reduction of natural resource consumption. In this sense, Product-Service Systems (PSS) can greatly contribute to achieving the Sustainable Development Goals, published by the United Nations. In this context, the present study aims to analyse the barriers associated with the operationalization of PSS business models in Brazil. Nineteen barriers listed in the literature supported a questionnaire used in a survey of Brazilian researchers. Data analysis was performed through Hierarchical Cluster Analysis, descriptive statistics and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). TOPSIS is used for comparative ordering of barriers in terms of intensity. The results showed that there are many barriers observed in the Brazilian scenario for the operationalization of PSS; those related to the resistance to change to the mentioned business models and the lack of necessary skills to manage them stood out. These findings can be used to enhance the debates about PSS in the Brazilian context.
Digital technologies are encouraging manufacturing companies in their servitization process by accelerating the offer of integrated product and service to create new value and grow relationships with customers in the Industry 4.0 era. In parallel, digitalization and servitization research streams are merging and paving the way for the Smart Product-Service System (Smart PSS). By analyzing the concept of Smart PSS, this paper questions the convergence between digital and service orientations and considers how digital technologies are used to enable decisions along the PSS lifecycle and/or at different planning levels. Applying topic modeling, a semi-systematic literature review on the combination of digital technologies with PSS has highlighted five main research streams: PSS design, digital servitization, assessing tool for PSS decisions, knowledge management along the lifecycle, sustainability business models. The development over time of these research streams has been tracked leading to the definition of a new research agenda on Smart PSS.
Innovation in society 5.0 is characterized by: innovations in service, or “servitization” open innovation; and frugal innovation. These innovations are related to the “bisociative act”. This chapter considers the stages that exist from the creation of a product idea and its commercialization, its subsequent acceptance by the customer, as well as the consequences of these various levels. Next, it focuses on the evolution of a product's stages and how to improve an existing product or a service. Sustainable innovation actually reflects numerous disruptions. Innovation based on a linear evolution of scientific knowledge is no longer prominent. That is why sustainable innovation is either social or frugal. It is becoming more and more related to the concept of mash‐up. The chapter explores how to implement mash‐up and mash‐up forms that are the consequence of the implementation of a new informational paradigm. Finally, it explores human resources that are an important point of the innovative organization.
Industry 4.0 manufacturing paradigm, apart from the technological revolution requires also a shift from the traditional education to an advanced set of methods for developing skills and building digital competences, summarized in the term Education 4.0. Human skills undoubtedly require upgrading to handle the key enabling technologies sufficiently, including machines as cyber-physical systems, augmented reality, human-robot collaboration, and smart devices. In the present work, these new requirements for the enhanced manufacturing workflow are presented. Furthermore, existing education 4.0 approaches are investigated, and finally, a Teaching Factory concept adapted to the Industry 4.0 paradigm needs is proposed.
This paper aims to present a landscape of interests that are emerging for the future of maintenance in Industrial Product-Service Systems. Across manufacturing industries there is growing interest to integrate products and services, where maintenance has a key role in delivering performance driven solutions (e.g. availability). It is observed that industry is aiming to gain competitive advantage, and the customer is increasingly intending to transfer the risks and uncertainties as reflected in contracts. The shift towards services is also putting more pressure on industry to accurately predict service requirements in terms of resulting cost and performance that enables the service provision. In light of these drivers, technologies and organisational themes are emerging to reduce uncertainty and cost for the in-service phase as explained and discussed in the paper.
More and more companies are beginning to move beyond manufacturing as a sole source of profit by offering integrated bundles of physical goods and services. This phenomenon has become popularly known as servitization, or the establishment of product–service systems (PSSs). Additionally, since the success of the Japanese after WWII and the subsequent popularization of the term “Lean Production” in the 1990s, lean too has almost become a nirvana for the majority of producers. Lean has also found its way into service operations, yet there is an apparent lack of knowledge when it comes to combining the successes associated with lean thinking with the potential of PSSs. Therefore, in this paper, we make use of two best-in-class lean companies that are recognized for excellence in both product and service offerings in order to analyse PSS operations in light of lean thinking. As such, we adopt a multiple case study approach in order to propose a framework for lean product-oriented product–service systems.